Using initial condition and model physics perturbations in short-range ensemble simulations of mesoscale convective systems

Two separate numerical model ensembles are created by using model configura tions with different model physical process parameterization schemes and id entical initial conditions, and by using different model initial conditions from a Monte Carlo approach and the identical model configuration. Simulat ions from these two ensembles are investigated for two 48-h periods during which large, long-lived mesoscale convective systems develop. These two per iods are chosen because, in some respects, they span the range of convectiv e forecast problems routinely handled by operational forecasters. Calculations of the root-mean-square error, equitable threat score, and ran ked probability score from both ensembles indicate that the model physics e nsemble is more skillful than the initial-condition ensemble when the large -scale forcing for upward motion is weak. When the large scale forcing for upward motion is strong, the initial-condition ensemble is more skillful th an the model physics ensemble. This result is consistent with the expectati on that model physics play a larger role in model simulations when the larg e-scale signal is weak and the assumptions used within the model parameteri zation schemes largely determine the evolution of the simulated weather eve nts. The variance from the two ensembles is created at significantly different r ates, with the variance in the physics ensemble being produced two to six t imes faster during the first 12 h than the variance in the initial-conditio n ensemble. Therefore, within a very brief time period, the variance from t he physics ensemble often greatly exceeds that produced by the initial-cond ition ensemble. These results suggest that varying the model physics is a p otentially powerful method to use in creating an ensemble. In essence, by u sing different model configurations, the systematic errors of the individua l ensemble members are different and, hence, this may allow one to determin e a probability density function from this ensemble that is more diffuse th an can be accomplished using a single model configuration.